Question the measurement
My LLM judge was flipping a coin a third of the time
What happened when I tested the judge I had trusted for months.
Software engineer / Miami, FL
The notebook
Small thoughts, things I learned the hard way, and ideas that needed more room.
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Question the measurement
What happened when I tested the judge I had trusted for months.
Follow a fix
An exact failure, the hardware behind it, and the two-line fix.
Look at the craft
The engineering behind a quieter page transition on this site.
4 pieces about Local models
I turned prefix caching back on for a 125B hybrid model on a DGX Spark, with the fixes carried from five open pull requests. Hits landed, a number planted deep in the prompt came back exactly every time, and an 11,000-token prompt dropped from 5.99 s to 0.96 s. Then one request answered the question two other requests were asking. Proving that was not a leak took a better instrument than the one I started with.
The model card advertised a 4B multi-token-prediction head. No published GGUF contained it, and the architecture had no code path to run it. Both halves arrived within 36 hours from two different strangers, in incompatible forms — so I merged the head into the target file myself. It went from 27.75 to 43.30 tok/s, and three of my four mistakes along the way were about verification, not tensors.
Four slots served my agentic pipeline at 1.2% prefix-cache reuse. One slot served the same pipeline at 86.7%, and saved 11.6 minutes of prefill in a 39-minute window. Slots schedule requests; they do not add compute — and on a single GPU they scatter the one thing a prefill-bound workload actually depends on.
I used a language model to pick the better of two drafts, and trusted it for months. Then I gave it two drafts from an identical configuration and asked it to choose. Here is what measuring a judge’s noise floor costs, and why every A/B result before it was unreadable.